Reliability-based robust design optimization of gap size of annular nuclear fuels using kriging and inverse distance weighting methods

Jaehyeok Doh, Younghoon Kim, Jongsoo Lee

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


In this study, the design optimization of the gap size of annular nuclear fuels used in pressurized water reactors (PWRs) was performed. For this, thermoelastic–plasticity–creep (TEPC) analysis of PWR annular fuels was carried out using an in-house code to investigate the performance of nuclear fuels. Surrogate models based on the kriging and inverse distance weighting models were generated using computational performance data based on optimal Latin hypercube design. Using these surrogate models, the gap size of PWR annular fuel was deterministically optimized using the micro-genetic algorithm to improve the heat transfer efficiency and maintain a lower level of stress. Reliability-based design optimization and reliability-based robust design optimization were conducted to satisfy target reliability and secure the robustness of the PWRs’ performance. The optimal gap size was validated through TEPC analysis and the optimum solutions were compared according to the approximate method and reliability index.

Original languageEnglish
Pages (from-to)2161-2176
Number of pages16
JournalEngineering Optimization
Issue number12
Publication statusPublished - 2018 Dec 2

Bibliographical note

Funding Information:
This research is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning [grant number 2017R1A2B4009606]; and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea [grant number 20163030024420].

Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics


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